During wafer fabrication, proper alignment of the wafer is important because it can help facilitate accurate and proper formation of layers on the wafer (e.g., metallic layers, substrate layers, etc.), resulting in a high product yield. For example, screen printers can be used to print layers (e.g., metal contacts) on solar wafers, which often need to be accurately printed over a selective emitter pattern. To align wafers, fiducial(s) on the wafer can be printed using lithography or other printing techniques, and then detected using machine vision and used to align the wafer in the manufacturing process.
However, the fiducials are sometimes degraded in the manufacturing process, resulting in blurry and/or erroneous fiducials that can be hard to detect using machine vision. As an example, additional layers of non-transparent material can be added over the fiducials before alignment. For example, a selective emitter pattern can be screen printed on the solar wafer (e.g., using a paste of silicon nanoparticles). Machine vision is used to detect portions of the selective emitter pattern for alignment. Further, after drying and before alignment, a layer of standard anti-reflective (A/R) coating is often added to the solar wafer over the selective emitter pattern. By the time the metal contacts are to be printed over the selective emitter pattern, the appearance of the alignment fiducials through the selective emitter pattern layer or in the selective emitter pattern has very poor visual contrast (e.g., due to the A/R coating), causing difficulty in detecting the fiducials and aligning the printing screen precisely to the selective emitter pattern.
Poor visual contrast of the layer deposition often makes identification of fiducials in a wafer (e.g., fiducials in the selective emitter pattern of a solar cell) difficult, in terms of both accuracy and robustness (e.g., for machine vision alignment systems). There are approaches known in the art for machine vision systems to be configured to compensate for these problems, but such compensation does not always work. For example, a machine vision system can be configured to use a low contrast threshold to find a fiducial pattern to compensate for poor contrast of the fiducial pattern. However, using a low contrast threshold can return false positive identifications for fiducial patterns. Identifying the “true” fiducial pattern from among many false positive identifications from each separate camera may be quite difficult. When multiple cameras are taken into consideration there can be a combinatorial number of possible matches (e.g. the correct match from each camera). In general, solving combinatorial problems can be quite time consuming because each possible identification from each camera would need to be considered.